Contrasting rule-based and similarity-based category learning: The effects of mood and prior knowledge on ambiguous categorization

This study adopts a dual-system view of category learning. The findings suggest that consumers who learn a dominant feature as a verbal rule for a product category will classify a new ambiguous product according to that feature even if it more closely resembles a different product category. The findings also demonstrate that dominant features can bias categorization toward a less prototypical category in the event that the new product breaks the rule. We refer to this phenomenon as criterial inferencing. Lastly, we offer unique empirical evidence to suggest that mood influences category learning and thus attenuates the criterial inferencing bias.     

Prior knowledge and subtyping effects in children's category learning

Two experiments examined how 5- and 10-year-old children revise their category representations when exposed to exemplars that are congruent or incongruent with existing knowledge. During training children were presented with exemplars containing features that were congruent or incongruent with children's social stereotypes together with a stereotype-neutral feature. In the knowledge-subtyping condition this neutral feature predicted the stereotype-congruence of the other features. In the knowledge-standard condition the neutral feature was uncorrelated with stereotype-congruence. At test children made judgements about feature co-occurrence within the learned category. In each experiment these judgements were influenced by both stereotypical beliefs and exemplar observation. Stereotypical beliefs, however, had a greater influence on co-occurrence judgements in the knowledge-subtyping than in the standard conditions. In Experiment 2 these effects were shown to generalize to judgements about features that were not presented during training. These results challenge current models of knowledge-based categorization by showing that exemplar structure determines whether novel exemplar features are incorporated into category representations.     

Putting together prior knowledge, verbal arguments, and observations in category learning

Two experiments addressed the novel issue of how people incorporate verbal arguments into category learning. In Experiment 1, at the start of learning, subjects were given verbal arguments, which had an influence equivalent to a fixed number of category members. In Experiment 2, subjects learned under slower paced conditions, and it was found that both prior knowledge and arguments had multiple effects on categorization: a fixed initial influence plus selective weighting of new observations. The results supported the idea that verbally presented arguments can be treated in a similar manner as other forms of prior knowledge, from the perspective of applying models of categorization.     

How prior knowledge affects selective attention during category learning: An eyetracking study

Research has shown that category learning is affected by (a) attention, which selects which aspects of stimuli are available for further processing, and (b) the existing semantic knowledge that learners bring to the task. However, little is known about how knowledge affects what is attended. Using eyetracking, we found that (a) knowledge indeed changes what features are attended, with knowledge-relevant features being fixated more often than irrelevant ones, (b) this effect was not due to an initial attentional bias toward relevant dimensions but rather emerged gradually as a result of observing category members, and (c) this effect grew even after a learning criterion was reached, that is, despite the absence of negative feedback. We argue that models of knowledge-based learning will remain incomplete until they specify mechanisms that dynamically select prior knowledge in response to observed category members and which then directs attention to knowledge-relevant dimensions and away from irrelevant ones.     

Children's conceptions of characteristic features of category members

The authors investigated children's conceptions of the characteristic features of category members and found that their conceptions underwent qualitative developmental changes. They hypothesized that (a) children initially tend to perceive category members in terms of individual characteristics (i.e., internal dispositions and behavioral patterns) and (b) only later do they conceive of category members in terms of their shared beliefs and values. They explored these hypotheses in 2 studies: In Study 1, they investigated how Protestant and Catholic children in Northern Ireland understood the religious intergroup context in their own country; in Study 2, children in Italy were presented with a fictional scenario of intergroup conflict and asked to explain the causes of the conflict. The results of both studies confirmed the 2 hypotheses.     

The effects of category overlap on information-integration and rule-based category learning

In three experiments, we investigated whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category-learning task requiring perceptual integration of information from multiple dimensions (an information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (a rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap--even when such strategies were suboptimal. In contrast, in the rule-based task, participants used explicit strategies, regardless of the level of category overlap. These data are consistent with a multiple systems view of category learning, and suggest that categorization strategy depends on the type of task that is used, and on the degree to which each stimulus is probabilistically associated with the contrasting categories.     

Category learning with minimal prior knowledge

In 6 experiments, the authors examined the use of prior knowledge in category learning. Previous studies of the effects of knowledge on category learning have used categories in which knowledge was related to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote" features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional explanation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge.     

特征的呈现方式对类别学习中规则和相似性知识获得的影响

采用原型变异任务,通过操纵规则和相似性特征的呈现通道,探讨特征的呈现方式对类别学习中规则和相似性知识获得的影响。结果发现,在听觉-视觉条件下习得规则的人数显著多于习得相似性的人数,而在视觉-听觉和视觉-视觉条件下则不存在显著差异;并且,三种条件下习得规则的正确率都高于习得相似性的正确率。这说明特征的呈现方式影响对规则和相似性特征的习得,在听觉通道呈现规则时被试更倾向基于规则分类。     

Feature distribution and background knowledge in category learning

Three experiments examined the interactions of category structure and prior knowledge in category learning.Experiment 1 examined the distribution of atypical or 'crossover' features in category learning. In real categories, crossover features may be unevenly distributed found primarily in very unusual examples of a category (like whales or ostriches). In contrast, in many psychology experiments, each item has exactly one crossover feature. Even versus uneven distribution of crossover features did not affect category learning when the features were neutral. However, when the features were connected by prior knowledge, it was much harder for subjects to learn the structure with the uneven distribution of crossover features. Experiments 2 and 3 found similar results with a slightly less uneven condition. We conclude that learning is a function of the interaction of category structure and prior knowledge rather than either one alone. Furthermore, knowledge benefits learning even when the category contains contradictions of the knowledge, so long as the contradictions are not very salient.     

Differential effects of regulatory fit on category learning

Motivation affects the degree to which people engage in tasks as well as the processes that they bring to bear. We explore the proposal that a fit between a person’s situationally induced self-regulatory focus and the reward structure of the task that they are pursuing supports greater flexibility in processing than does a mismatch between regulatory focus and reward structure. In two experiments, we prime regulatory focus and manipulate task reward structure. Our participants perform a rule-based learning task whose solution requires flexible strategy testing as well as an information-integration task for which flexible strategy use hinders learning. Across two experiments, we predict and obtain a three-way interaction between regulatory focus, reward structure, and task. Relative to a mismatch, a match leads to better rule-based task performance, but worse performance on the information-integration task. We relate these findings to other work on motivation and choking under pressure.     

The effects of concurrent task interference on category learning: evidence for multiple category learning systems

Participants learned simple and complex category structures under typical single-task conditions and when performing a simultaneous numerical Stroop task. In the simple categorization tasks, each set of contrasting categories was separated by a unidimensional explicit rule, whereas the complex tasks required integrating information from three stimulus dimensions and resulted in implicit rules that were difficult to verbalize. The concurrent Stroop task dramatically impaired learning of the simple explicit rules, but did not significantly delay learning of the complex implicit rules. These results support the hypothesis that category learning is mediated by multiple learning systems.     

Restructuring partitioned knowledge: the role of recoordination in category learning

Knowledge restructuring refers to changes in the strategy with which people solve a given problem. Two types of knowledge restructuring are supported by existing category learning models. The first is a relearning process, which involves incremental updating of knowledge as learning progresses. The second is a recoordination process, which involves novel changes in the way existing knowledge is applied to the task. Whereas relearning is supported by both single- and multiple-module models of category learning, only multiple-module models support recoordination. To date, only relearning has been directly supported empirically. We report two category learning experiments that provide direct evidence of recoordination. People can fluidly alternate between different categorization strategies, and moreover, can reinstate an old strategy even after prolonged use of an alternative. The knowledge restructuring data are not well fit by a single-module model (ALCOVE). By contrast, a multiple-module model (ATRIUM) quantitatively accounts for recoordination. Low-level changes in the distribution of dimensional attention are shown to subsequently affect how ATRIUM coordinates its modular knowledge. We argue that learning about complex tasks occurs at the level of the partial knowledge elements used to generate a response strategy.     

Subtyping as a knowledge preservation strategy in category learning

Subtyping occurs when atypical examples are excluded from consideration in judging a category. In three experiments, we investigated whether subtyping can influence category learning. In each experiment, participants learned about a category where most, but not all, of the exemplars corresponded to a theme. The category structure included a subtyping dimension, which had one value for theme-congruent exemplars and another for exception exemplars. On the basis of work by Hayes, Foster, and Gadd (2003) and studies on social stereotyping, we hypothesized that this subtyping dimension would enable the participants to discount the exception exemplars, thereby facilitating category learning. Contrary to expectations, we did not find a subtyping effect with traditional category-learning procedures. By introducing the theme prior to learning, however, we observed increased effects on typicality ratings (Experiment 1) and facilitated learning of the category (Experiment 2). Experiment 3 provided direct evidence that introducing the theme prior to learning enhanced the subtyping effect and provided support for a knowledge-gating explanation of subtyping. We conclude that subtyping effects are strongest on already-learned concepts and that subtyping is unlikely to aid in the learning of new concepts, except in particular circumstances.     

Modeling cross-situational word-referent learning: prior questions

Both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together.     

Prototypes in category learning: the effects of category size, category structure, and stimulus complexity

Although research in categorization has sometimes been motivated by prototype theory, recent studies have favored exemplar theory. However, some of these studies focused on small, poorly differentiated categories composed of simple, 4-dimensional stimuli. Some analyzed the aggregate data of entire groups. Some compared powerful multiplicative exemplar models to less powerful additive prototype models. Here, comparable prototype and exemplar models were fit to individual-participant data in 4 experiments that sampled category sets varying in size, level of category structure, and stimulus complexity (dimensionality). The prototype model always fit the observed data better than the exemplar model did. Prototype-based processes seemed especially relevant when participants learned categories that were larger or contained more complex stimuli.     

Explanation and prior knowledge interact to guide learning

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in allowing participants to relate prior knowledge to patterns underlying category membership. The experiments revealed a superadditive interaction between explanation and informative labels, with explainers who received informative labels most likely to discover (Experiments 1 and 2) and generalize (Experiments 3 and 4) a pattern consistent with prior knowledge. However, explainers were no more likely than controls to discover multiple patterns (Experiments 1 and 2), indicating that effects of explanation are relatively targeted. We suggest that explanation recruits prior knowledge to assess whether candidate patterns are likely to have broad scope (i.e., to generalize within and beyond study observations). This interpretation is supported by the finding that effects of explanation on prior knowledge were attenuated when learners believed prior knowledge was irrelevant to generalizing category membership (Experiment 4). This research provides evidence that explanation can serve as a mechanism for deploying prior knowledge to assess the scope of observed patterns.     

Delayed feedback effects on rule-based and information-integration category learning

The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.     

Contrast and accentuation effects in category learning

This study examines the accentuation of perceived intercategory differences. In Experiment 1, 2 sets of trait adjectives were presented--a neutral set and a set of either favorable traits or unfavorable traits. Ss estimated the mean favorability of each set. The mean favorability of the neutral set was then increased or decreased by adding new traits. As predicted, the estimated mean favorability of the neutral set changed more when the set became more distinct from a contextual set than when it became more similar. In Experiment 2, estimated category means were displaced away from each other (contrast effect), and they moved even farther apart when new information increased the variability of trait favorability (accentuation effect). This change was illusory because the actual category means remained constant. Experiment 3, in which trait adjectives described members of 2 novel groups, replicated Experiment 2. The relevance of contrast and accentuation effects to the development and maintenance of differentiated intergroup perceptions is discussed.     

The role of prior knowledge and curiosity in learning

Psychonomic Bulletin & Review - Recent work has argued that curiosity can improve learning. However, these studies also leave open the possibility that being on the verge of...     

Reading strategies and prior knowledge in learning from hypertext

In two experiments, we identified two main strategies followed by hypertext readers in selecting their reading orders. The first consisted in selecting the text semantically related to the previously read section (coherence strategy), and the second in choosing the most interesting text, delaying reading of less interesting sections (interest strategy). Comprehension data revealed that these strategies affected learning differently as a function of the reader's prior knowledge. For low-knowledge readers, the coherence strategy supported better learning of the content. This effect seems to rely on the improvement of reading order coherence induced by this strategy. By contrast, for intermediate-knowledge readers the coherence and the interest strategies benefited comprehension equally. In both cases, learning was supported through the active processing induced by these strategies. Discussion focuses on resolving inconsistencies in the literature concerning whether or not hypertext supports better comprehension than does traditional linear texts.